In this report, we analyzed the hazard risk from coastal flooding in Menlo Park. Menlo Park is a city at the eastern edge of San Mateo County. The population is about 35,000. A considerable number of people live in areas close to the coastal. They are at great risk in the event of rising sea level and the occasional return of flood. The report mainly focus on the estimation of vehicles average annual loss caused by flood from 2020 to 2050.
To get the hazard scenarios, we used data from OCOF and cropped to the extent of Menlo Park. We considered the permutations and combinations of 3 SLRs(Sea Level Rise) and 3RPs(Return Period), a total of 9 scenarios.
Here, we took SLR=50 and RP=100 as an example, and drew the flood depth map.
It can be seen that when Sea Level Rise is 50 and Return Period is 100, some buildings in Menlo Park would be affected by floods, especially those in the northeast part.
Next, we repeated this process and obtained a total of 9 maps. Please check this link to see the specific content of these maps: https://hhyj4495.shinyapps.io/dashboard_flood_menlo_park/
So far, we have collected the hazard data for Menlo Park. In order to get the number of vehicles, we did the following steps: First, using the latest ACS 5-yr data about vehicle ownership in Menlo Park, and produce an estimate of the total number of owned vehicles. Second, using the EMFAC data to estimate the growth of vehicles. We assumed that the % vehicle ownership rate doesn’t change over the next 30 years.
Then, we identified the census blocks within Menlo Park, and use OpenStreetMap data to retrieve all building footprints within these blocks. Assume that the exposure based on building footprints does not change over the study period. Then, we use 2020 Decennial census data to calculate the total population in each block.
| Year | Vehicle Category | Fuel Type | Population | percentage |
|---|---|---|---|---|
| 2020 | LDA | Gasoline | 2698816 | 1.000000 |
| 2030 | LDA | Gasoline | 2714691 | 1.005883 |
| 2040 | LDA | Gasoline | 2818585 | 1.044379 |
| 2050 | LDA | Gasoline | 2962226 | 1.097602 |
| GEOID20 | pop |
|---|---|
| 060816121011000 | 25 |
| 060816121011001 | 34 |
| 060816121011003 | 133 |
| 060816121011004 | 70 |
| 060816121011006 | 9 |
| 060816121011007 | 17 |
We assumed that buildings with type != NA are all residential buildings. This is because non-residential buildings are marked with their corresponding types in OpenStreetMap data set. Then we allocated 2020 vehicles from the whole CBG to each building, assuming population is distributed evenly across buildings in a block, and vehicles are distributed evenly across population.
| GEOID20 | cbg | bldg_count | pop | vehicle_count | veh_per_person | ppl_per_bldg | veh_per_bldg |
|---|---|---|---|---|---|---|---|
| 060816116001001 | 060816116001 | 55 | 82 | 774 | 0.6604096 | 1.490909 | 0.9846106 |
| 060816116001002 | 060816116001 | 41 | 75 | 774 | 0.6604096 | 1.829268 | 1.2080663 |
| 060816116001003 | 060816116001 | 40 | 81 | 774 | 0.6604096 | 2.025000 | 1.3373294 |
| 060816116001004 | 060816116001 | 57 | 104 | 774 | 0.6604096 | 1.824561 | 1.2049578 |
| 060816116001005 | 060816116001 | 40 | 101 | 774 | 0.6604096 | 2.525000 | 1.6675341 |
| 060816116001006 | 060816116001 | 20 | 55 | 774 | 0.6604096 | 2.750000 | 1.8161263 |
Base on this, we got the number of vehicles corresponding to each building in Menlo Park. We created a data frame to save these information. For each building, we recorded its osm_id, the number of vehicles of this building, and its location.
Next, we collected vulnerability data on the relationship between flood depth and vehicle damage from: https//planning.erdc.dren.mil/toolbox/library.cfm?Option=Listing&Type=EGM&Search=Policy&Sort=Default
We took the data of vehicle type = sedans to simplify the calculation.
| depth | perc_damage | moe |
|---|---|---|
| 0.0 | 0.000 | 0.0000 |
| 0.5 | 0.076 | 0.0242 |
| 1.0 | 0.280 | 0.0184 |
| 2.0 | 0.462 | 0.0151 |
| 3.0 | 0.622 | 0.0145 |
| 4.0 | 0.760 | 0.0157 |
| 5.0 | 0.876 | 0.0174 |
| 6.0 | 0.970 | 0.0192 |
| 7.0 | 1.000 | 0.0206 |
| 8.0 | 1.000 | 0.0206 |
| 9.0 | 1.000 | 0.0206 |
| 10.0 | 1.000 | 0.0206 |
The table above states that if the depth above ground is 3, then the percentage of damage would be 62.2%.
After obtaining all the above information, we got the average flood depth of each building under different SLR and RP conditions. Next, we took the flood depth of each building as input values, and obtained the corresponding percent damage from the table vulnerability, flood depth and vehicle damage by means of linear interpolation.
Assumed that all vehicles are parked on the ground floor (no basement, no underground parking), so the flood depth suffered by the building is equivalent to the flood depth suffered by the vehicles.
Next, we created an interactive plot to show the relationship between damaged vehicles and PR/SLR. In order to do that, a data frame is created, which has every combination of OpenStreetMap ID, sea level rise, and return period.
As is shown in the plot above, we can drag the pointer to set the SLR to 0, 25 or 50. It can be seen that when the SLR is 25m the average of percent damage is about 30%-40%, while when the SLR is 50, the average of percent damage exceeds 50%.
In the next step, we quantified these losses and estimated the average annualized loss in $ vehicle damages in Menlo Park from 2020 to 2050. To simplify the calculation, we made several assumptions: 1) The average cost of owning a car is $14,571 according to a U.S. News and World Report study. 2) Pickup trucks accounted for 20.57 percent of all vehicles in operation, according to analysis by Experian Automotive. The data can be found in Experian Automotive’s AutoCount Vehicles in Operation database. So we assume that 20.57% of the vehicles are immune to the hazard. 3) We assume that 25% of the vehicles are likely to be moved away from the hazard exposure with advanced warning.
Therefore: \[vehicle\ damage\ in\ USD = (1-percent\ move) * (1-percent\ immune) *cost\ per\ vehicle* percent\ damage \]
| osm_id | SLR | damage |
|---|---|---|
| 123925032 | 000 | 0.2258485 |
| 123925065 | 000 | 0.4893384 |
| 48232776 | 000 | 21.1813977 |
| 123925034 | 000 | 295.2190495 |
| 123925051 | 000 | 32.5361313 |
| 123925061 | 000 | 302.8075703 |
Here, we use RCP 4.5 occurrence rates of sea level rise in the Bay Area across years.
| SLR | 2020 | 2030 | 2040 |
|---|---|---|---|
| 0 | 0.942 | 0.923 | 0.793 |
| 25 | 0.000 | 0.051 | 0.198 |
| 50 | 0.000 | 0.000 | 0.001 |
| 75 | 0.000 | 0.000 | 0.000 |
| 100 | 0.000 | 0.000 | 0.000 |
| 125 | 0.000 | 0.000 | 0.000 |
| 150 | 0.000 | 0.000 | 0.000 |
| 175 | 0.000 | 0.000 | 0.000 |
| 200 | 0.000 | 0.000 | 0.000 |
| 500 | 0.000 | 0.000 | 0.000 |
When predicting $ vehicle damages, we mainly considered two factors, one is the increase in the number of vehicles, and the other is the change in SLR. The change in the number of vehicles has been estimated by EMFAC, and the change of SLR could be estimated from the above table, which has the probability of each level of SLR. Based on this, we could get the annual average loss of vehicles of each building in 2020, 2030, 2040 and 2050.
In the above map, we took the difference between the AALs in 2050 and 2020. As is shown, the AAL in 2050 is significantly higher than the AAL in 2020. In the coastal area of Menlo Park, the AALs are larger in the northwest, where some buildings have AALs that have grown by close to $2,000. By contrast, the AALs in the southeast are relatively negligible, and almost all buildings have changes in AALs that are less than 100USD.
We summarized these buildings into the CBGs they belong to, and got the above map. CBG in the southwest of Menlo Park has the largest AAL, and its average annualized loss across 99 buildings is $35,000 from 2020 to 2050. This may be due to the high density of vehicles, or the high flood depth. In order to solve these problems and avoid potential damages, adding urban drainage facilities or multistorey car park might be necessary.
As we found that table vulnerability, flood depth and vehicle damage contains information about the standard deviations, we performed a Monte Carlo simulation to propagate the uncertainty in the depth-damage relationship. For each building, we performed 1000 simulations.
Then, we took the average of perc_damage from 1000 simulations. In this way, the damage percentage of vehicles of each building is obtained.
| perc_damage | avg_depth | osm_id | SLR | RP | |
|---|---|---|---|---|---|
| 69636 | 0.0000000 | 0.0000000 | 123925032 | 000 | 001 |
| 69645 | 0.0000000 | 0.0000000 | 123925065 | 000 | 001 |
| 25570 | 0.0008310 | 0.0055747 | 48232776 | 000 | 020 |
| 696361 | 0.0000000 | 0.0000000 | 123925032 | 000 | 020 |
| 69637 | 0.0517056 | 0.3468819 | 123925034 | 000 | 020 |
| 69641 | 0.0000000 | 0.0000000 | 123925051 | 000 | 020 |
| perc_damage | avg_depth | osm_id | SLR | RP | |
|---|---|---|---|---|---|
| 69636 | 0.0000000 | 0.0000000 | 123925032 | 000 | 001 |
| 69645 | 0.0000000 | 0.0000000 | 123925065 | 000 | 001 |
| 25570 | 0.0008474 | 0.0055747 | 48232776 | 000 | 020 |
| 696361 | 0.0000000 | 0.0000000 | 123925032 | 000 | 020 |
| 69637 | 0.0527261 | 0.3468819 | 123925034 | 000 | 020 |
| 69641 | 0.0000000 | 0.0000000 | 123925051 | 000 | 020 |
There is a certain degree of change in perc_damage before and after Monte Carlo simulation, bt due to the small MOE, the change in perc_damage is not that sensitive to it. However, the resistance of the vehicle to flood depth is still a very important factor as it directly affects the AAL.
Also, there are other sources of uncertainty exist but outside the scope of our analysis.
| factor1 | Improvement of vehicle waterproofing system | possitive |
| factor2 | Upgrading of urban water supply and drainage system | possitive |
| factor3 | Relocation of housing in coastal areas | possitive |
| factor4 | # multistorey car park | positive |
| factor5 | Increased global warming leads to more dramatic sea level rise | negative |
| factor6 | Return period shorteded | negative |
We found that the AAL is not that sensitive to vulnerability, flood depth and vehicle damage, and AAL would not be affected much after 1000 simulations. It might because AAL is more affected by RP, SLR, as well as population growth, rising number of vehicles, rising vehicle prices and other factors.
The following two tables list the changes of the 6 buildings before and after Monte Carlo simulation.
| osm_id | 2020 | 2030 | 2040 | 2050 | change |
|---|---|---|---|---|---|
| 123925032 | 181.5261 | 243.7674 | 423.2651 | 818.3913 | 636.8651 |
| 123925065 | 175.4775 | 234.4028 | 404.1044 | 777.2328 | 601.7553 |
| 48232776 | 822.2192 | 947.2753 | 1278.4944 | 1952.3367 | 1130.1175 |
| 123925034 | 1500.5869 | 1661.5731 | 2059.7681 | 2820.3242 | 1319.7373 |
| 123925051 | 992.6452 | 1128.5814 | 1482.2611 | 2188.9755 | 1196.3303 |
| 123925061 | 1248.2215 | 1390.3225 | 1746.7622 | 2439.7866 | 1191.5651 |
| osm_id | 2020 | 2030 | 2040 | 2050 | change |
|---|---|---|---|---|---|
| 123925032 | 184.5968 | 247.1031 | 427.2173 | 823.4194 | 638.8226 |
| 123925065 | 178.4902 | 237.6897 | 408.0396 | 782.3248 | 603.8345 |
| 48232776 | 825.7069 | 950.9014 | 1282.3216 | 1956.2437 | 1130.5368 |
| 123925034 | 1503.6081 | 1664.5793 | 2062.5386 | 2822.3582 | 1318.7501 |
| 123925051 | 995.3785 | 1131.4171 | 1485.2357 | 2191.9687 | 1196.5902 |
| 123925061 | 1252.6926 | 1394.8350 | 1751.1198 | 2443.4532 | 1190.7606 |
We noticed that there are many households with only 1 vehicle or no vehicle. So we estimate the number of 0-vehicle and 1-vehicle households in Menlo Park that are exposed to some amount of flood risk.
The map below showed the distribution of such household, from 2020 to 2050.